Borrageiro, G; Firoozye, N; Barucca, P; (2023) Online Learning with Radial Basis Function Networks. Journal of Financial Data Science , 5 (1) pp. 76-95. 10.3905/jfds.2022.1.112.

Text Borrageiro et al. - Online learning with radial basis function networks - 2022.pdf - Accepted Version Access restricted to UCL open access staff Download (1MB)

Abstract

The authors provide multi-horizon forecasts on the returns of financial time series. Their sequen-tially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Their RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. The authors show that the training set financial time series returns have low similarity with their test set counterparts, highlighting the challenges faced in particular by kernel-based methods that use the training set returns as test-time prototypes; in contrast, their online learning RBFNets have hidden units that retain greater similarity across time.

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